To utilize the review for professional malicious user detections, we employ a state-of-the-art RNN model, Hierarchical Dual-Attention RNN (HDAN) [1]}, [2]}, to project the review into a sentiment score. The structure of HDAN is shown in Fig.REF . HDAN calculates update gate \(ug_t\) , reset gate \(re_t\)  and temporary state \(\widetilde{h}_{t-1}\)  Eq.(REF ), Eq.(REF ), Eq.(REF ):
<FIGURE>\(\begin{aligned}u{g_t} = \sigma ({W_{ug}}\widehat{y} + {U_{ug}}{h_{t - 1}} + {b_{ug}}),\end{aligned}\) 
\(\begin{aligned}{\widetilde{h}_{t - 1}} = \tanh ({W_h}\widehat{y} + {re_t} \odot ({U_h}{h_{t - 1}}) + {b_h}),\end{aligned}\) 
\(\begin{aligned}r{e_t} = \sigma ({W_{re}}\widehat{y} + {U_{re}}{h_{t - 1}} + {b_{re}}),\end{aligned}\) 
